Growth Engineer (with Computer Science Background

The Fabulous goal is to help people all over the world master the art of behavior change and build healthy habits. In joining the team, you will become a part of helping millions achieve their goals and change their lives.

At Fabulous, our Growth team packs a one‑two punch. Our team is made up of behavioral scientists and growth product managers. We’re unique in the tech / health & wellness world, applying behavioral insights to help better our product, inform our experimentation, and optimize our growth work.

• Support the entire experimentation processes of the Growth team. Code, analyze and work in every facet of the Growth work streams. Collaborate with the Product Development team and the Data Science team. Earn and train in the skills of growth product management / product management.
• Peer-reviewing Growth code and assisting in code review for Growth experiment.
• Assisting with experiment launches on Google Firebase.
• Debugging code and working closely with the product development team.
• Assist in QAing new features prior to releases.
• Peer-reviewing experiment analysis.
• Owning the translations of winning experiments into other languages / across platforms, and owning this experimentation process from start to finish.

• Has a background in software engineering.
• Is very confident in JSON & JAVA or Java script (SQL is bonus).
• Is comfortable with statistical significance and common types of statistical bias.
• Understands the basics of good experimental design.
• Is passionate about growth, metrics, and providing an outstanding user experience.
• Is data‑driven and analytical. Our team strives to find user patterns and leverage them to optimize key metrics and user experience.
• Passion for learning and an interest in behavioral science is a bonus.

• Ideally, has a bachelor’s degree in statistics, computer science, engineering, any technical major, or related work experience.
• Has designed and run AB / RCT trials before. Understand the nuances of good experimental design.
• Background in data science is a bonus.
• Background / interest in ML.